





'''simple docstring'''






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)


lowerCAmelCase									=   """https://openaipublic.azureedge.net/jukebox/models/"""
lowerCAmelCase									=   {
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}






def   __A    (				a_    :	Union[str, Any]						):
		if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 1_0:
				lowerCAmelCase  :     Dict       						= key.replace(".model.1.bias"   ,".conv1d_1.bias"						)
		elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 1_0:
				lowerCAmelCase  :     int       						= key.replace(".model.1.weight"   ,".conv1d_1.weight"						)
		elif key.endswith(".model.3.bias"						) and len(key.split("."						)						) > 1_0:
				lowerCAmelCase  :     Union[str, Any]       						= key.replace(".model.3.bias"   ,".conv1d_2.bias"						)
		elif key.endswith(".model.3.weight"						) and len(key.split("."						)						) > 1_0:
				lowerCAmelCase  :     str       						= key.replace(".model.3.weight"   ,".conv1d_2.weight"						)

		if "conditioner_blocks.0." in key:
				lowerCAmelCase  :     int       						= key.replace("conditioner_blocks.0"   ,"conditioner_blocks"						)

		if "prime_prior" in key:
				lowerCAmelCase  :     List[Any]       						= key.replace("prime_prior"   ,"encoder"						)

		if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
				lowerCAmelCase  :     Optional[int]       						= key.replace(".emb."   ,"."						)

		if key.endswith("k"						):  # replace vqvae.X.k with vqvae.X.codebook
				return key.replace(".k"   ,".codebook"						)
		if "y_emb." in key:
				return key.replace("y_emb."   ,"metadata_embedding."						)

		if "x_emb.emb." in key:
				lowerCAmelCase  :     Optional[int]       						= key.replace("0.x_emb.emb"   ,"embed_tokens"						)

		if "prime_state_ln" in key:
				return key.replace("prime_state_ln"   ,"encoder.final_layer_norm"						)
		if ".ln" in key:
				return key.replace(".ln"   ,".layer_norm"						)
		if "_ln" in key:
				return key.replace("_ln"   ,"_layer_norm"						)

		if "prime_state_proj" in key:
				return key.replace("prime_state_proj"   ,"encoder.proj_in"						)
		if "prime_x_out" in key:
				return key.replace("prime_x_out"   ,"encoder.lm_head"						)
		if "prior.x_out" in key:
				return key.replace("x_out"   ,"fc_proj_out"						)
		if "x_emb" in key:
				return key.replace("x_emb"   ,"embed_tokens"						)

		return key






def   __A    (				a_    :	Any   ,a_    :	Optional[Any]   ,a_    :	Any   ,a_    :	str						):
		lowerCAmelCase  :     Dict       						= {}
		import re

		lowerCAmelCase  :     Optional[int]       						= re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
		lowerCAmelCase  :     str       						= re.compile(
		    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
		lowerCAmelCase  :     List[Any]       						= re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

		lowerCAmelCase  :     int       						= re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
		lowerCAmelCase  :     Union[str, Any]       						= re.compile(
		    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
		lowerCAmelCase  :     Tuple       						= re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

		lowerCAmelCase  :     Optional[Any]       						= re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"						)
		lowerCAmelCase  :     Any       						= re.compile(
		    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
		lowerCAmelCase  :     Dict       						= re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"						)

		for original_key, value in state_dict.items():
				# rename vqvae.encoder keys
				if re_encoder_block_conv_in.fullmatch(a_						):
						lowerCAmelCase  :     Optional[Any]       						= re_encoder_block_conv_in.match(a_						)
						lowerCAmelCase  :     Optional[int]       						= regex_match.groups()
						lowerCAmelCase  :     Dict       						= int(groups[2]						) * 2 + int(groups[3]						)
						lowerCAmelCase  :     Optional[int]       						= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'''
						lowerCAmelCase  :     List[Any]       						= re_encoder_block_conv_in.sub(a_   ,a_						)

				elif re_encoder_block_resnet.fullmatch(a_						):
						lowerCAmelCase  :     Optional[int]       						= re_encoder_block_resnet.match(a_						)
						lowerCAmelCase  :     str       						= regex_match.groups()
						lowerCAmelCase  :     Union[str, Any]       						= int(groups[2]						) * 2 + int(groups[3]						)
						lowerCAmelCase  :     Optional[int]       						= {"1": 1, "3": 2}[groups[-2]]
						lowerCAmelCase  :     Optional[int]       						= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
						lowerCAmelCase  :     Optional[Any]       						= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
						lowerCAmelCase  :     Union[str, Any]       						= prefix + resnet_block
						lowerCAmelCase  :     int       						= re_encoder_block_resnet.sub(a_   ,a_						)

				elif re_encoder_block_proj_out.fullmatch(a_						):
						lowerCAmelCase  :     Optional[int]       						= re_encoder_block_proj_out.match(a_						)
						lowerCAmelCase  :     Any       						= regex_match.groups()
						lowerCAmelCase  :     Optional[Any]       						= f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
						lowerCAmelCase  :     Optional[int]       						= re_encoder_block_proj_out.sub(a_   ,a_						)

				# rename vqvae.decoder keys
				elif re_decoder_block_conv_out.fullmatch(a_						):
						lowerCAmelCase  :     List[Any]       						= re_decoder_block_conv_out.match(a_						)
						lowerCAmelCase  :     int       						= regex_match.groups()
						lowerCAmelCase  :     Union[str, Any]       						= int(groups[2]						) * 2 + int(groups[3]						) - 2
						lowerCAmelCase  :     Optional[int]       						= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
						lowerCAmelCase  :     str       						= re_decoder_block_conv_out.sub(a_   ,a_						)

				elif re_decoder_block_resnet.fullmatch(a_						):
						lowerCAmelCase  :     Any       						= re_decoder_block_resnet.match(a_						)
						lowerCAmelCase  :     Optional[int]       						= regex_match.groups()
						lowerCAmelCase  :     List[str]       						= int(groups[2]						) * 2 + int(groups[3]						) - 2
						lowerCAmelCase  :     str       						= {"1": 1, "3": 2}[groups[-2]]
						lowerCAmelCase  :     Any       						= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
						lowerCAmelCase  :     Union[str, Any]       						= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
						lowerCAmelCase  :     Union[str, Any]       						= prefix + resnet_block
						lowerCAmelCase  :     Optional[int]       						= re_decoder_block_resnet.sub(a_   ,a_						)

				elif re_decoder_block_proj_in.fullmatch(a_						):
						lowerCAmelCase  :     Union[str, Any]       						= re_decoder_block_proj_in.match(a_						)
						lowerCAmelCase  :     str       						= regex_match.groups()
						lowerCAmelCase  :     Optional[Any]       						= f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
						lowerCAmelCase  :     List[Any]       						= re_decoder_block_proj_in.sub(a_   ,a_						)

				# rename prior cond.model to upsampler.upsample_block and resnet
				elif re_prior_cond_conv_out.fullmatch(a_						):
						lowerCAmelCase  :     Tuple       						= re_prior_cond_conv_out.match(a_						)
						lowerCAmelCase  :     Any       						= regex_match.groups()
						lowerCAmelCase  :     Dict       						= int(groups[1]						) * 2 + int(groups[2]						) - 2
						lowerCAmelCase  :     Optional[int]       						= f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
						lowerCAmelCase  :     Any       						= re_prior_cond_conv_out.sub(a_   ,a_						)

				elif re_prior_cond_resnet.fullmatch(a_						):
						lowerCAmelCase  :     Optional[int]       						= re_prior_cond_resnet.match(a_						)
						lowerCAmelCase  :     List[str]       						= regex_match.groups()
						lowerCAmelCase  :     List[Any]       						= int(groups[1]						) * 2 + int(groups[2]						) - 2
						lowerCAmelCase  :     Optional[int]       						= {"1": 1, "3": 2}[groups[-2]]
						lowerCAmelCase  :     Tuple       						= f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
						lowerCAmelCase  :     List[Any]       						= f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
						lowerCAmelCase  :     Dict       						= prefix + resnet_block
						lowerCAmelCase  :     Optional[int]       						= re_prior_cond_resnet.sub(a_   ,a_						)

				elif re_prior_cond_proj_in.fullmatch(a_						):
						lowerCAmelCase  :     Optional[Any]       						= re_prior_cond_proj_in.match(a_						)
						lowerCAmelCase  :     Optional[Any]       						= regex_match.groups()
						lowerCAmelCase  :     Tuple       						= f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
						lowerCAmelCase  :     Optional[Any]       						= re_prior_cond_proj_in.sub(a_   ,a_						)

				# keep original key
				else:
						lowerCAmelCase  :     List[str]       						= original_key

				lowerCAmelCase  :     List[str]       						= replace_key(a_						)

				if f'''{key_prefix}.{key}''' not in model_state_dict or key is None:
						print(f'''failed converting {original_key} to {key}, does not match'''						)

				# handle missmatched shape
				elif value.shape != model_state_dict[f'''{key_prefix}.{key}'''].shape:
						lowerCAmelCase  :     int       						= model_state_dict[f'''{key_prefix}.{key}''']
						print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''						)
						lowerCAmelCase  :     int       						= original_key

				lowerCAmelCase  :     List[str]       						= original_key
				lowerCAmelCase  :     Optional[Any]       						= value

		return new_dict






@torch.no_grad()
def   __A    (				a_    :	Union[str, Any]=None   ,a_    :	str=None						):
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''						):
						lowerCAmelCase  :     Tuple       						= requests.get(f'''{PREFIX}{file}'''   ,allow_redirects=a_						)
						os.makedirs(f'''{pytorch_dump_folder_path}/'''   ,exist_ok=a_						)
						open(f'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''   ,"wb"						).write(r.content						)

		lowerCAmelCase  :     List[Any]       						= MODEL_MAPPING[model_name.split("/"						)[-1]]

		lowerCAmelCase  :     Dict       						= JukeboxConfig.from_pretrained(a_						)
		lowerCAmelCase  :     Union[str, Any]       						= JukeboxModel(a_						)

		lowerCAmelCase  :     Dict       						= []
		lowerCAmelCase  :     Optional[int]       						= {}
		for i, dict_name in enumerate(a_						):
				lowerCAmelCase  :     Optional[Any]       						= torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"						)[-1]}'''						)["model"]

				lowerCAmelCase  :     Tuple       						= {}
				for k in old_dic.keys():
						if k.endswith(".b"						):
								lowerCAmelCase  :     Any       						= old_dic[k]
						elif k.endswith(".w"						):
								lowerCAmelCase  :     Dict       						= old_dic[k]
						elif "level_2" not in dict_name and "cond.model." in k:
								lowerCAmelCase  :     Optional[Any]       						= old_dic[k]
						else:
								lowerCAmelCase  :     Union[str, Any]       						= old_dic[k]

				lowerCAmelCase  :     Union[str, Any]       						= "vqvae" if i == 0 else f'''priors.{3 - i}'''
				lowerCAmelCase  :     List[Any]       						= fix_jukebox_keys(a_   ,model.state_dict()   ,a_   ,a_						)
				weight_dict.append(a_						)

		lowerCAmelCase  :     int       						= weight_dict.pop(0						)
		model.vqvae.load_state_dict(a_						)
		for i in range(len(a_						)						):
				model.priors[i].load_state_dict(weight_dict[2 - i]						)

		Path(a_						).mkdir(exist_ok=a_						)
		with open(f'''{pytorch_dump_folder_path}/mapping.json'''   ,"w"						) as txtfile:
				json.dump(a_   ,a_						)

		print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''						)
		model.save_pretrained(a_						)

		return weight_dict


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--model_name""",
					    default="""jukebox-5b-lyrics""",
					    type=str,
					    help="""Name of the model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default="""jukebox-5b-lyrics-converted""",
					    type=str,
					    help="""Path to the output PyTorch model directory.""",
					)
					lowerCAmelCase									=   parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {"""configuration_mra""": ["""MRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MraConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """MRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MraForMaskedLM""",
					    """MraForMultipleChoice""",
					    """MraForQuestionAnswering""",
					    """MraForSequenceClassification""",
					    """MraForTokenClassification""",
					    """MraLayer""",
					    """MraModel""",
					    """MraPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mra import (
										    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MraForMaskedLM,
										    MraForMultipleChoice,
										    MraForQuestionAnswering,
										    MraForSequenceClassification,
										    MraForTokenClassification,
										    MraLayer,
										    MraModel,
										    MraPreTrainedModel,
										)
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure)



